Abstract
Decision trees are considered as an efficient technique to ex- press classification knowledge and to use it. However, their most standard algorithms do not deal with uncertainty, especially the cognitive one. In this paper, we develop a method to adapt the decision tree technique to the case where the object’s classes are not exactly known, and where the uncertainty about the class’ value is represented by a belief function. The adaptation concerns both the construction of the tree and its use to classify new objects characterized by uncertain attribute values.
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Elouedi1, Z., Mellouli1, K., Smets, P. (2000). Classification with Belief Decision Trees. In: Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2000. Lecture Notes in Computer Science, vol 1904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45331-8_8
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DOI: https://doi.org/10.1007/3-540-45331-8_8
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